
"""simple docstring"""


import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoProcessor,
    AutoTokenizer,
    BertTokenizer,
    ProcessorMixin,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available


sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402
from test_module.custom_processing import CustomProcessor  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


SCREAMING_SNAKE_CASE			  =  get_tests_dir("""fixtures/dummy_feature_extractor_config.json""")
SCREAMING_SNAKE_CASE			  =  get_tests_dir("""fixtures/vocab.json""")
SCREAMING_SNAKE_CASE			  =  get_tests_dir("""fixtures""")



class 	__a	(   unittest.TestCase						):
       UpperCamelCase_		:       str				=      ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]      )->	Any:







              """simple docstring"""
              UpperCamelCase       			=		0






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple      )->	Union[str, Any]:







              """simple docstring"""
              UpperCamelCase       			=		AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h"      )
              self.assertIsInstance(UpperCAmelCase_							,   UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	List[Any]:







              """simple docstring"""
              with tempfile.TemporaryDirectory() as tmpdirname:
                     UpperCamelCase       			=		WavaVecaConfig()
                     UpperCamelCase       			=		AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h"      )

                     # save in new folder
                     model_config.save_pretrained(UpperCAmelCase_      )
                     processor.save_pretrained(UpperCAmelCase_      )

                     UpperCamelCase       			=		AutoProcessor.from_pretrained(UpperCAmelCase_      )

              self.assertIsInstance(UpperCAmelCase_							,   UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	int:







              """simple docstring"""
              with tempfile.TemporaryDirectory() as tmpdirname:
                     # copy relevant files
                     copyfile(UpperCAmelCase_							,   os.path.join(UpperCAmelCase_							,   UpperCAmelCase_      )      )
                     copyfile(UpperCAmelCase_							,   os.path.join(UpperCAmelCase_							,   "vocab.json"      )      )

                     UpperCamelCase       			=		AutoProcessor.from_pretrained(UpperCAmelCase_      )

              self.assertIsInstance(UpperCAmelCase_							,   UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str      )->	List[str]:







              """simple docstring"""
              with tempfile.TemporaryDirectory() as tmpdirname:
                     UpperCamelCase       			=		WavaVecaFeatureExtractor()
                     UpperCamelCase       			=		AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h"      )

                     UpperCamelCase       			=		WavaVecaProcessor(UpperCAmelCase_							,   UpperCAmelCase_      )

                     # save in new folder
                     processor.save_pretrained(UpperCAmelCase_      )

                     # drop `processor_class` in tokenizer
                     with open(os.path.join(UpperCAmelCase_							,   UpperCAmelCase_      )							,   "r"      ) as f:
                            UpperCamelCase       			=		json.load(UpperCAmelCase_      )
                            config_dict.pop("processor_class"      )

                     with open(os.path.join(UpperCAmelCase_							,   UpperCAmelCase_      )							,   "w"      ) as f:
                            f.write(json.dumps(UpperCAmelCase_      )      )

                     UpperCamelCase       			=		AutoProcessor.from_pretrained(UpperCAmelCase_      )

              self.assertIsInstance(UpperCAmelCase_							,   UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict      )->	Optional[Any]:







              """simple docstring"""
              with tempfile.TemporaryDirectory() as tmpdirname:
                     UpperCamelCase       			=		WavaVecaFeatureExtractor()
                     UpperCamelCase       			=		AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h"      )

                     UpperCamelCase       			=		WavaVecaProcessor(UpperCAmelCase_							,   UpperCAmelCase_      )

                     # save in new folder
                     processor.save_pretrained(UpperCAmelCase_      )

                     # drop `processor_class` in feature extractor
                     with open(os.path.join(UpperCAmelCase_							,   UpperCAmelCase_      )							,   "r"      ) as f:
                            UpperCamelCase       			=		json.load(UpperCAmelCase_      )
                            config_dict.pop("processor_class"      )

                     with open(os.path.join(UpperCAmelCase_							,   UpperCAmelCase_      )							,   "w"      ) as f:
                            f.write(json.dumps(UpperCAmelCase_      )      )

                     UpperCamelCase       			=		AutoProcessor.from_pretrained(UpperCAmelCase_      )

              self.assertIsInstance(UpperCAmelCase_							,   UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	List[Any]:







              """simple docstring"""
              with tempfile.TemporaryDirectory() as tmpdirname:
                     UpperCamelCase       			=		WavaVecaConfig(processor_class="Wav2Vec2Processor"      )
                     model_config.save_pretrained(UpperCAmelCase_      )
                     # copy relevant files
                     copyfile(UpperCAmelCase_							,   os.path.join(UpperCAmelCase_							,   "vocab.json"      )      )
                     # create emtpy sample processor
                     with open(os.path.join(UpperCAmelCase_							,   UpperCAmelCase_      )							,   "w"      ) as f:
                            f.write("{}"      )

                     UpperCamelCase       			=		AutoProcessor.from_pretrained(UpperCAmelCase_      )

              self.assertIsInstance(UpperCAmelCase_							,   UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	List[str]:







              """simple docstring"""
              # If remote code is not set, we will time out when asking whether to load the model.
              with self.assertRaises(UpperCAmelCase_      ):
                     UpperCamelCase       			=		AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor"      )
              # If remote code is disabled, we can't load this config.
              with self.assertRaises(UpperCAmelCase_      ):
                     UpperCamelCase       			=		AutoProcessor.from_pretrained(
                         "hf-internal-testing/test_dynamic_processor"							,   trust_remote_code=UpperCAmelCase_      )

              UpperCamelCase       			=		AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor"							,   trust_remote_code=UpperCAmelCase_      )
              self.assertTrue(processor.special_attribute_present      )
              self.assertEqual(processor.__class__.__name__							,   "NewProcessor"      )

              UpperCamelCase       			=		processor.feature_extractor
              self.assertTrue(feature_extractor.special_attribute_present      )
              self.assertEqual(feature_extractor.__class__.__name__							,   "NewFeatureExtractor"      )

              UpperCamelCase       			=		processor.tokenizer
              self.assertTrue(tokenizer.special_attribute_present      )
              if is_tokenizers_available():
                     self.assertEqual(tokenizer.__class__.__name__							,   "NewTokenizerFast"      )

                     # Test we can also load the slow version
                     UpperCamelCase       			=		AutoProcessor.from_pretrained(
                         "hf-internal-testing/test_dynamic_processor"							,   trust_remote_code=UpperCAmelCase_							,   use_fast=UpperCAmelCase_      )
                     UpperCamelCase       			=		new_processor.tokenizer
                     self.assertTrue(new_tokenizer.special_attribute_present      )
                     self.assertEqual(new_tokenizer.__class__.__name__							,   "NewTokenizer"      )
              else:
                     self.assertEqual(tokenizer.__class__.__name__							,   "NewTokenizer"      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]      )->	Dict:







              """simple docstring"""
              try:
                     AutoConfig.register("custom"							,   UpperCAmelCase_      )
                     AutoFeatureExtractor.register(UpperCAmelCase_							,   UpperCAmelCase_      )
                     AutoTokenizer.register(UpperCAmelCase_							,   slow_tokenizer_class=UpperCAmelCase_      )
                     AutoProcessor.register(UpperCAmelCase_							,   UpperCAmelCase_      )
                     # Trying to register something existing in the Transformers library will raise an error
                     with self.assertRaises(UpperCAmelCase_      ):
                            AutoProcessor.register(UpperCAmelCase_							,   UpperCAmelCase_      )

                     # Now that the config is registered, it can be used as any other config with the auto-API
                     UpperCamelCase       			=		CustomFeatureExtractor.from_pretrained(UpperCAmelCase_      )

                     with tempfile.TemporaryDirectory() as tmp_dir:
                            UpperCamelCase       			=		os.path.join(UpperCAmelCase_							,   "vocab.txt"      )
                            with open(UpperCAmelCase_							,   "w"							,   encoding="utf-8"      ) as vocab_writer:
                                   vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]      )      )
                            UpperCamelCase       			=		CustomTokenizer(UpperCAmelCase_      )

                     UpperCamelCase       			=		CustomProcessor(UpperCAmelCase_							,   UpperCAmelCase_      )

                     with tempfile.TemporaryDirectory() as tmp_dir:
                            processor.save_pretrained(UpperCAmelCase_      )
                            UpperCamelCase       			=		AutoProcessor.from_pretrained(UpperCAmelCase_      )
                            self.assertIsInstance(UpperCAmelCase_							,   UpperCAmelCase_      )

              finally:
                     if "custom" in CONFIG_MAPPING._extra_content:
                            del CONFIG_MAPPING._extra_content["custom"]
                     if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                            del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
                     if CustomConfig in TOKENIZER_MAPPING._extra_content:
                            del TOKENIZER_MAPPING._extra_content[CustomConfig]
                     if CustomConfig in PROCESSOR_MAPPING._extra_content:
                            del PROCESSOR_MAPPING._extra_content[CustomConfig]






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	int:







              """simple docstring"""



              class 	__a	(   _lowerCAmelCase						):
                     UpperCamelCase_		:       Any				=      False





              class 	__a	(   _lowerCAmelCase						):
                     UpperCamelCase_		:       Tuple				=      False





              class 	__a	(   _lowerCAmelCase						):
                     UpperCamelCase_		:       Dict				=      '''AutoFeatureExtractor'''
                     UpperCamelCase_		:       Optional[int]				=      '''AutoTokenizer'''
                     UpperCamelCase_		:       int				=      False

              try:
                     AutoConfig.register("custom"							,   UpperCAmelCase_      )
                     AutoFeatureExtractor.register(UpperCAmelCase_							,   UpperCAmelCase_      )
                     AutoTokenizer.register(UpperCAmelCase_							,   slow_tokenizer_class=UpperCAmelCase_      )
                     AutoProcessor.register(UpperCAmelCase_							,   UpperCAmelCase_      )
                     # If remote code is not set, the default is to use local classes.
                     UpperCamelCase       			=		AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor"      )
                     self.assertEqual(processor.__class__.__name__							,   "NewProcessor"      )
                     self.assertFalse(processor.special_attribute_present      )
                     self.assertFalse(processor.feature_extractor.special_attribute_present      )
                     self.assertFalse(processor.tokenizer.special_attribute_present      )

                     # If remote code is disabled, we load the local ones.
                     UpperCamelCase       			=		AutoProcessor.from_pretrained(
                         "hf-internal-testing/test_dynamic_processor"							,   trust_remote_code=UpperCAmelCase_      )
                     self.assertEqual(processor.__class__.__name__							,   "NewProcessor"      )
                     self.assertFalse(processor.special_attribute_present      )
                     self.assertFalse(processor.feature_extractor.special_attribute_present      )
                     self.assertFalse(processor.tokenizer.special_attribute_present      )

                     # If remote is enabled, we load from the Hub.
                     UpperCamelCase       			=		AutoProcessor.from_pretrained(
                         "hf-internal-testing/test_dynamic_processor"							,   trust_remote_code=UpperCAmelCase_      )
                     self.assertEqual(processor.__class__.__name__							,   "NewProcessor"      )
                     self.assertTrue(processor.special_attribute_present      )
                     self.assertTrue(processor.feature_extractor.special_attribute_present      )
                     self.assertTrue(processor.tokenizer.special_attribute_present      )

              finally:
                     if "custom" in CONFIG_MAPPING._extra_content:
                            del CONFIG_MAPPING._extra_content["custom"]
                     if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                            del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
                     if CustomConfig in TOKENIZER_MAPPING._extra_content:
                            del TOKENIZER_MAPPING._extra_content[CustomConfig]
                     if CustomConfig in PROCESSOR_MAPPING._extra_content:
                            del PROCESSOR_MAPPING._extra_content[CustomConfig]






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str      )->	Union[str, Any]:







              """simple docstring"""
              UpperCamelCase       			=		AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-bert"      )
              self.assertEqual(processor.__class__.__name__							,   "BertTokenizerFast"      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-convnext"      )
              self.assertEqual(processor.__class__.__name__							,   "ConvNextImageProcessor"      )





@is_staging_test
class 	__a	(   unittest.TestCase						):
       UpperCamelCase_		:       Dict				=      ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']






       @classmethod
       def 						_SCREAMING_SNAKE_CASE							(	cls			:				Optional[Any]      )->	str:







              """simple docstring"""
              UpperCamelCase       			=		TOKEN
              HfFolder.save_token(UpperCAmelCase_      )






       @classmethod
       def 						_SCREAMING_SNAKE_CASE							(	cls			:				Optional[Any]      )->	List[str]:







              """simple docstring"""
              try:
                     delete_repo(token=cls._token							,   repo_id="test-processor"      )
              except HTTPError:
                     pass

              try:
                     delete_repo(token=cls._token							,   repo_id="valid_org/test-processor-org"      )
              except HTTPError:
                     pass

              try:
                     delete_repo(token=cls._token							,   repo_id="test-dynamic-processor"      )
              except HTTPError:
                     pass






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	Any:







              """simple docstring"""
              UpperCamelCase       			=		WavaVecaProcessor.from_pretrained(UpperCAmelCase_      )
              with tempfile.TemporaryDirectory() as tmp_dir:
                     processor.save_pretrained(
                         os.path.join(UpperCAmelCase_							,   "test-processor"      )							,   push_to_hub=UpperCAmelCase_							,   use_auth_token=self._token      )

                     UpperCamelCase       			=		WavaVecaProcessor.from_pretrained(f"{USER}/test-processor"      )
                     for k, v in processor.feature_extractor.__dict__.items():
                            self.assertEqual(UpperCAmelCase_							,   getattr(new_processor.feature_extractor							,   UpperCAmelCase_      )      )
                     self.assertDictEqual(new_processor.tokenizer.get_vocab()							,   processor.tokenizer.get_vocab()      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	Optional[int]:







              """simple docstring"""
              UpperCamelCase       			=		WavaVecaProcessor.from_pretrained(UpperCAmelCase_      )

              with tempfile.TemporaryDirectory() as tmp_dir:
                     processor.save_pretrained(
                         os.path.join(UpperCAmelCase_							,   "test-processor-org"      )							,   push_to_hub=UpperCAmelCase_							,   use_auth_token=self._token							,   organization="valid_org"							,   )

                     UpperCamelCase       			=		WavaVecaProcessor.from_pretrained("valid_org/test-processor-org"      )
                     for k, v in processor.feature_extractor.__dict__.items():
                            self.assertEqual(UpperCAmelCase_							,   getattr(new_processor.feature_extractor							,   UpperCAmelCase_      )      )
                     self.assertDictEqual(new_processor.tokenizer.get_vocab()							,   processor.tokenizer.get_vocab()      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]      )->	int:







              """simple docstring"""
              CustomFeatureExtractor.register_for_auto_class()
              CustomTokenizer.register_for_auto_class()
              CustomProcessor.register_for_auto_class()

              UpperCamelCase       			=		CustomFeatureExtractor.from_pretrained(UpperCAmelCase_      )

              with tempfile.TemporaryDirectory() as tmp_dir:
                     UpperCamelCase       			=		os.path.join(UpperCAmelCase_							,   "vocab.txt"      )
                     with open(UpperCAmelCase_							,   "w"							,   encoding="utf-8"      ) as vocab_writer:
                            vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]      )      )
                     UpperCamelCase       			=		CustomTokenizer(UpperCAmelCase_      )

              UpperCamelCase       			=		CustomProcessor(UpperCAmelCase_							,   UpperCAmelCase_      )

              with tempfile.TemporaryDirectory() as tmp_dir:
                     create_repo(f"{USER}/test-dynamic-processor"							,   token=self._token      )
                     UpperCamelCase       			=		Repository(UpperCAmelCase_							,   clone_from=f"{USER}/test-dynamic-processor"							,   token=self._token      )
                     processor.save_pretrained(UpperCAmelCase_      )

                     # This has added the proper auto_map field to the feature extractor config
                     self.assertDictEqual(
                         processor.feature_extractor.auto_map							,   {
                             "AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor",
                             "AutoProcessor": "custom_processing.CustomProcessor",
                         }							,   )

                     # This has added the proper auto_map field to the tokenizer config
                     with open(os.path.join(UpperCAmelCase_							,   "tokenizer_config.json"      )      ) as f:
                            UpperCamelCase       			=		json.load(UpperCAmelCase_      )
                     self.assertDictEqual(
                         tokenizer_config["auto_map"]							,   {
                             "AutoTokenizer": ["custom_tokenization.CustomTokenizer", None],
                             "AutoProcessor": "custom_processing.CustomProcessor",
                         }							,   )

                     # The code has been copied from fixtures
                     self.assertTrue(os.path.isfile(os.path.join(UpperCAmelCase_							,   "custom_feature_extraction.py"      )      )      )
                     self.assertTrue(os.path.isfile(os.path.join(UpperCAmelCase_							,   "custom_tokenization.py"      )      )      )
                     self.assertTrue(os.path.isfile(os.path.join(UpperCAmelCase_							,   "custom_processing.py"      )      )      )

                     repo.push_to_hub()

              UpperCamelCase       			=		AutoProcessor.from_pretrained(f"{USER}/test-dynamic-processor"							,   trust_remote_code=UpperCAmelCase_      )
              # Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
              self.assertEqual(new_processor.__class__.__name__							,   "CustomProcessor"      )







"""simple docstring"""


from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
      import datasets



class 	__a	(   _lowerCAmelCase						):






       @slow
       @require_torch
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	Dict:







              """simple docstring"""
              UpperCamelCase       			=		EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny"							,   "prajjwal1/bert-tiny"      )
              UpperCamelCase       			=		BertTokenizer.from_pretrained("bert-base-uncased"      )

              UpperCamelCase       			=		bertabert.config.encoder.vocab_size
              UpperCamelCase       			=		tokenizer.sep_token_id
              UpperCamelCase       			=		tokenizer.cls_token_id
              UpperCamelCase       			=		128

              UpperCamelCase       			=		datasets.load_dataset("cnn_dailymail"							,   "3.0.0"							,   split="train[:1%]"      )
              UpperCamelCase       			=		datasets.load_dataset("cnn_dailymail"							,   "3.0.0"							,   split="validation[:1%]"      )

              UpperCamelCase       			=		train_dataset.select(range(32      )      )
              UpperCamelCase       			=		val_dataset.select(range(16      )      )

              UpperCamelCase       			=		4

              def _map_to_encoder_decoder_inputs(UpperCAmelCase_			:				Any      ):
                     # Tokenizer will automatically set [BOS] <text> [EOS]
                     UpperCamelCase       			=		tokenizer(batch["article"]							,   padding="max_length"							,   truncation=UpperCAmelCase_							,   max_length=512      )
                     UpperCamelCase       			=		tokenizer(batch["highlights"]							,   padding="max_length"							,   truncation=UpperCAmelCase_							,   max_length=128      )
                     UpperCamelCase       			=		inputs.input_ids
                     UpperCamelCase       			=		inputs.attention_mask

                     UpperCamelCase       			=		outputs.input_ids
                     UpperCamelCase       			=		outputs.input_ids.copy()
                     UpperCamelCase       			=		[
                         [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
                     ]
                     UpperCamelCase       			=		outputs.attention_mask

                     assert all(len(UpperCAmelCase_      ) == 512 for x in inputs.input_ids      )
                     assert all(len(UpperCAmelCase_      ) == 128 for x in outputs.input_ids      )

                     return batch

              def _compute_metrics(UpperCAmelCase_			:				Any      ):
                     UpperCamelCase       			=		pred.label_ids
                     UpperCamelCase       			=		pred.predictions

                     # all unnecessary tokens are removed
                     UpperCamelCase       			=		tokenizer.batch_decode(UpperCAmelCase_							,   skip_special_tokens=UpperCAmelCase_      )
                     UpperCamelCase       			=		tokenizer.batch_decode(UpperCAmelCase_							,   skip_special_tokens=UpperCAmelCase_      )

                     UpperCamelCase       			=		sum([int(pred_str[i] == label_str[i]      ) for i in range(len(UpperCAmelCase_      )      )]      ) / len(UpperCAmelCase_      )

                     return {"accuracy": accuracy}

              # map train dataset
              UpperCamelCase       			=		train_dataset.map(
                  _map_to_encoder_decoder_inputs							,   batched=UpperCAmelCase_							,   batch_size=UpperCAmelCase_							,   remove_columns=["article", "highlights"]							,   )
              train_dataset.set_format(
                  type="torch"							,   columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]							,   )

              # same for validation dataset
              UpperCamelCase       			=		val_dataset.map(
                  _map_to_encoder_decoder_inputs							,   batched=UpperCAmelCase_							,   batch_size=UpperCAmelCase_							,   remove_columns=["article", "highlights"]							,   )
              val_dataset.set_format(
                  type="torch"							,   columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]							,   )

              UpperCamelCase       			=		self.get_auto_remove_tmp_dir()

              UpperCamelCase       			=		SeqaSeqTrainingArguments(
                  output_dir=UpperCAmelCase_							,   per_device_train_batch_size=UpperCAmelCase_							,   per_device_eval_batch_size=UpperCAmelCase_							,   predict_with_generate=UpperCAmelCase_							,   evaluation_strategy="steps"							,   do_train=UpperCAmelCase_							,   do_eval=UpperCAmelCase_							,   warmup_steps=0							,   eval_steps=2							,   logging_steps=2							,   )

              # instantiate trainer
              UpperCamelCase       			=		SeqaSeqTrainer(
                  model=UpperCAmelCase_							,   args=UpperCAmelCase_							,   compute_metrics=_compute_metrics							,   train_dataset=UpperCAmelCase_							,   eval_dataset=UpperCAmelCase_							,   tokenizer=UpperCAmelCase_							,   )

              # start training
              trainer.train()





